The goal of Fair Representation Learning (FRL) is to mitigate biases in machine learning models by learning data representations that enable high accuracy on downstream tasks while minimizing discrimination based on sensitive attributes. The evaluation of FRL methods in many recent works primarily focuses on the tradeoff between downstream fairness and accuracy with respect to a single task that was used to approximate the utility of representations during training (proxy task). This incentivizes retaining only features relevant to the proxy task while discarding all other information. In extreme cases, this can cause the learned representations to collapse to a trivial, binary value, rendering them unusable in transfer settings. In this work, we argue that this approach is fundamentally mismatched with the original motivation of FRL, which arises from settings with many downstream tasks unknown at training time (transfer tasks). To remedy this, we propose to refocus the evaluation protocol of FRL methods primarily around the performance on transfer tasks. A key challenge when conducting such an evaluation is the lack of adequate benchmarks. We address this by formulating four criteria that a suitable evaluation procedure should fulfill. Based on these, we propose TransFair, a benchmark that satisfies these criteria, consisting of novel variations of popular FRL datasets with carefully calibrated transfer tasks. In this setting, we reevaluate state-of-the-art FRL methods, observing that they often overfit to the proxy task, which causes them to underperform on certain transfer tasks. We further highlight the importance of task-agnostic learning signals for FRL methods, as they can lead to more transferrable representations.

翻译：公平表示学习（FRL）的目标是通过学习数据表示来缓解机器学习模型中的偏见，这些表示能够在保持下游任务高准确率的同时，最小化基于敏感属性的歧视。近年来许多工作中对FRL方法的评估主要聚焦于下游公平性与准确率之间的权衡，且通常仅针对训练时用于近似表示效用的单一任务（代理任务）。这种做法激励模型仅保留与代理任务相关的特征，而丢弃所有其他信息。在极端情况下，这可能导致学习到的表示坍缩为平凡的二元值，使其在迁移场景中无法使用。本文认为，这种方法与FRL的原始动机存在根本性错配——FRL本应适用于训练时存在大量未知下游任务（迁移任务）的场景。为纠正这一问题，我们建议将FRL方法的评估重点重新聚焦于迁移任务的性能。实施此类评估的关键挑战在于缺乏合适的基准测试。为此，我们提出了合格评估流程应满足的四项标准，并据此构建了TransFair基准测试。该基准通过精心校准的迁移任务，对流行的FRL数据集进行创新性重构，完全符合上述标准。在此设定下，我们对前沿FRL方法进行重新评估，发现它们往往对代理任务过拟合，导致在某些迁移任务上表现不佳。我们进一步强调了任务无关学习信号对FRL方法的重要性，因其能够产生更具迁移能力的表示。